System and Method for Performance Regression Detection

2. The method of claim 1, wherein the first state execution data comprises execution time data and execution count data corresponding to executions of the feature when a feature flag wrapping the feature is in a first state corresponding to the updated version of the feature.

3. The method of claim 2, wherein the second state execution data comprises execution time data and execution count data corresponding to executions of the feature when the feature flag is in a second state corresponding to the original version of the feature.

4. The method of claim 1, wherein in response to determining that the second execution time metric is greater than the first execution time metric, determining that the updated version of the feature introduces a performance improvement.

5. The method of claim 1, wherein determining that the first execution time metric is greater than the second execution time metric comprises determining whether the first execution time metric is greater than the second execution time metric by at least a threshold value.

7. The method of claim 6, wherein in response to determining that the first execution time metric is greater than the second execution time metric by the second threshold, disabling the updated version of the feature.

8. The method of claim 7, wherein disabling the updated version of the feature comprises setting a feature flag wrapping the feature to a disabled state.

9. The method of claim 1, further comprising generating an alert that the updated version of the feature has introduced a performance regression in the software application.

10. The method of claim 1, further comprising determining that execution count of the first execution data satisfies a threshold prior to determining whether the first execution time metric is greater than the second execution time metric.

12. The method of claim 11, wherein the first execution data comprises an average execution time of executions of the updated version of the feature.

13. The method of claim 11, wherein the second execution data comprises an average execution time of executions of the original version of the feature.

14. The method of claim 11, further comprising generating a notification to a code owner of the updated version of the feature that the feature flag has been disabled.

15. The method of claim 11, further comprising generating a notification to a code owner of the updated version of the feature that the updated version of the feature introduces the performance regression.

16. The method of claim 11, further comprising determining from the first execution data and the second execution data that the updated version of the feature introduces a performance improvement that exceeds a third threshold, and, in response, setting the feature flag to a enabled state.

18. The method of claim 17, wherein the first state is associated with an updated version of a feature of the software application.

19. The method of claim 18, wherein the second state is associated with an original version of the feature of the software application.

20. The method of claim 17, wherein determining from the first execution data and the second execution data that the first state of the feature flag introduces a performance regression comprises determining that a first execution time metric determine from the first execution data is greater than a second execution time metric determine from the second execution data.